


import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import YolosForObjectDetection, YolosModel
 from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor

class     A__   :







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case=1_3     ,   __snake_case=[3_0, 3_0]     ,   __snake_case=2     ,   __snake_case=3     ,   __snake_case=True     ,   __snake_case=True     ,   __snake_case=3_2     ,   __snake_case=5     ,   __snake_case=4     ,   __snake_case=3_7     ,   __snake_case="gelu"     ,   __snake_case=0.1     ,   __snake_case=0.1     ,   __snake_case=1_0     ,   __snake_case=0.02     ,   __snake_case=3     ,   __snake_case=None     ,   __snake_case=8     ,   __snake_case=1_0     ,   ):
         snake_case											=							parent
         snake_case											=							batch_size
         snake_case											=							image_size
         snake_case											=							patch_size
         snake_case											=							num_channels
         snake_case											=							is_training
         snake_case											=							use_labels
         snake_case											=							hidden_size
         snake_case											=							num_hidden_layers
         snake_case											=							num_attention_heads
         snake_case											=							intermediate_size
         snake_case											=							hidden_act
         snake_case											=							hidden_dropout_prob
         snake_case											=							attention_probs_dropout_prob
         snake_case											=							type_sequence_label_size
         snake_case											=							initializer_range
         snake_case											=							num_labels
         snake_case											=							scope
         snake_case											=							n_targets
         snake_case											=							num_detection_tokens
         # we set the expected sequence length (which is used in several tests)
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
         snake_case											=							(image_size[1] // patch_size) * (image_size[0] // patch_size)
         snake_case											=							num_patches + 1 + self.num_detection_tokens
    def 						a_     (			self       ):
         snake_case											=							floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]       )

         snake_case											=							None
         if self.use_labels:
              # labels is a list of Dict (each Dict being the labels for a given example in the batch)
              snake_case											=							[]
              for i in range(self.batch_size       ):
                   snake_case											=							{}
                   snake_case											=							torch.randint(
                       high=self.num_labels     ,   size=(self.n_targets,)     ,   device=__snake_case       )
                   snake_case											=							torch.rand(self.n_targets     ,   4     ,   device=__snake_case       )
                   labels.append(__snake_case       )

         snake_case											=							self.get_config()

         return config, pixel_values, labels
    def 						a_     (			self       ):
         return YolosConfig(
             image_size=self.image_size     ,   patch_size=self.patch_size     ,   num_channels=self.num_channels     ,   hidden_size=self.hidden_size     ,   num_hidden_layers=self.num_hidden_layers     ,   num_attention_heads=self.num_attention_heads     ,   intermediate_size=self.intermediate_size     ,   hidden_act=self.hidden_act     ,   hidden_dropout_prob=self.hidden_dropout_prob     ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,   is_decoder=__snake_case     ,   initializer_range=self.initializer_range     ,   num_detection_tokens=self.num_detection_tokens     ,   num_labels=self.num_labels     ,   )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							YolosModel(config=__snake_case       )
         model.to(__snake_case       )
         model.eval()
         snake_case											=							model(__snake_case       )
         self.parent.assertEqual(
             result.last_hidden_state.shape     ,   (self.batch_size, self.expected_seq_len, self.hidden_size)       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							YolosForObjectDetection(__snake_case       )
         model.to(__snake_case       )
         model.eval()

         snake_case											=							model(pixel_values=__snake_case       )
         snake_case											=							model(__snake_case       )

         self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_detection_tokens, self.num_labels + 1)       )
         self.parent.assertEqual(result.pred_boxes.shape     ,   (self.batch_size, self.num_detection_tokens, 4)       )

         snake_case											=							model(pixel_values=__snake_case     ,   labels=__snake_case       )

         self.parent.assertEqual(result.loss.shape     ,   ()       )
         self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.num_detection_tokens, self.num_labels + 1)       )
         self.parent.assertEqual(result.pred_boxes.shape     ,   (self.batch_size, self.num_detection_tokens, 4)       )




    def 						a_     (			self       ):
         snake_case											=							self.prepare_config_and_inputs()
         snake_case					,    snake_case					,    snake_case											=							config_and_inputs
         snake_case											=							{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_torch
class     A__   (       snake_case__			,   snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
    __magic_name__         =						(
        {'feature-extraction': YolosModel, 'object-detection': YolosForObjectDetection} if is_torch_available() else {}
    )

    __magic_name__         =						False
    __magic_name__         =						False
    __magic_name__         =						False
    __magic_name__         =						False
    def 						a_     (			self     ,   __snake_case     ,   __snake_case     ,   __snake_case=False       ):
         snake_case											=							super()._prepare_for_class(__snake_case     ,   __snake_case     ,   return_labels=__snake_case       )

         if return_labels:
              if model_class.__name__ == "YolosForObjectDetection":
                   snake_case											=							[]
                   for i in range(self.model_tester.batch_size       ):
                        snake_case											=							{}
                        snake_case											=							torch.ones(
                            size=(self.model_tester.n_targets,)     ,   device=__snake_case     ,   dtype=torch.long       )
                        snake_case											=							torch.ones(
                            self.model_tester.n_targets     ,   4     ,   device=__snake_case     ,   dtype=torch.float       )
                        labels.append(__snake_case       )
                   snake_case											=							labels

         return inputs_dict
    def 						a_     (			self       ):
         snake_case											=							YolosModelTester(self       )
         snake_case											=							ConfigTester(self     ,   config_class=__snake_case     ,   has_text_modality=__snake_case     ,   hidden_size=3_7       )
    def 						a_     (			self       ):
         self.config_tester.run_common_tests()
    def 						a_     (			self       ):
         # YOLOS does not use inputs_embeds
         pass
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              snake_case											=							model_class(__snake_case       )
              self.assertIsInstance(model.get_input_embeddings()     ,   (nn.Module)       )
              snake_case											=							model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(__snake_case     ,   nn.Linear       )       )
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              snake_case											=							model_class(__snake_case       )
              snake_case											=							inspect.signature(model.forward       )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              snake_case											=							[*signature.parameters.keys()]

              snake_case											=							['''pixel_values''']
              self.assertListEqual(arg_names[:1]     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__snake_case       )
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()
         snake_case											=							True

         # in YOLOS, the seq_len is different
         snake_case											=							self.model_tester.expected_seq_len
         for model_class in self.all_model_classes:
              snake_case											=							True
              snake_case											=							False
              snake_case											=							True
              snake_case											=							model_class(__snake_case       )
              model.to(__snake_case       )
              model.eval()
              with torch.no_grad():
                   snake_case											=							model(**self._prepare_for_class(__snake_case     ,   __snake_case       )       )
              snake_case											=							outputs.attentions
              self.assertEqual(len(__snake_case       )     ,   self.model_tester.num_hidden_layers       )

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              snake_case											=							True
              snake_case											=							model_class(__snake_case       )
              model.to(__snake_case       )
              model.eval()
              with torch.no_grad():
                   snake_case											=							model(**self._prepare_for_class(__snake_case     ,   __snake_case       )       )
              snake_case											=							outputs.attentions
              self.assertEqual(len(__snake_case       )     ,   self.model_tester.num_hidden_layers       )

              self.assertListEqual(
                  list(attentions[0].shape[-3:]       )     ,   [self.model_tester.num_attention_heads, seq_len, seq_len]     ,   )
              snake_case											=							len(__snake_case       )

              # Check attention is always last and order is fine
              snake_case											=							True
              snake_case											=							True
              snake_case											=							model_class(__snake_case       )
              model.to(__snake_case       )
              model.eval()
              with torch.no_grad():
                   snake_case											=							model(**self._prepare_for_class(__snake_case     ,   __snake_case       )       )

              snake_case											=							1
              self.assertEqual(out_len + added_hidden_states     ,   len(__snake_case       )       )

              snake_case											=							outputs.attentions

              self.assertEqual(len(__snake_case       )     ,   self.model_tester.num_hidden_layers       )
              self.assertListEqual(
                  list(self_attentions[0].shape[-3:]       )     ,   [self.model_tester.num_attention_heads, seq_len, seq_len]     ,   )
    def 						a_     (			self       ):
         def check_hidden_states_output(__snake_case     ,   __snake_case     ,   __snake_case       ):
              snake_case											=							model_class(__snake_case       )
              model.to(__snake_case       )
              model.eval()

              with torch.no_grad():
                   snake_case											=							model(**self._prepare_for_class(__snake_case     ,   __snake_case       )       )

              snake_case											=							outputs.hidden_states

              snake_case											=							getattr(
                  self.model_tester     ,   '''expected_num_hidden_layers'''     ,   self.model_tester.num_hidden_layers + 1       )
              self.assertEqual(len(__snake_case       )     ,   __snake_case       )

              # YOLOS has a different seq_length
              snake_case											=							self.model_tester.expected_seq_len

              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]       )     ,   [seq_length, self.model_tester.hidden_size]     ,   )

         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              snake_case											=							True
              check_hidden_states_output(__snake_case     ,   __snake_case     ,   __snake_case       )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              snake_case											=							True

              check_hidden_states_output(__snake_case     ,   __snake_case     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_object_detection(*__snake_case       )




    @slow
    def 						a_     (			self       ):
         for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case											=							YolosModel.from_pretrained(__snake_case       )
              self.assertIsNotNone(__snake_case       )






def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
     return image

@require_torch
@require_vision
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    @cached_property
    def 						a_     (			self       ):
         return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''       ) if is_vision_available() else None




    @slow
    def 						a_     (			self       ):
         snake_case											=							YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''       ).to(__snake_case       )

         snake_case											=							self.default_image_processor
         snake_case											=							prepare_img()
         snake_case											=							image_processor(images=__snake_case     ,   return_tensors='''pt'''       ).to(__snake_case       )

         # forward pass
         with torch.no_grad():
              snake_case											=							model(inputs.pixel_values       )

         # verify outputs
         snake_case											=							torch.Size((1, 1_0_0, 9_2)       )
         self.assertEqual(outputs.logits.shape     ,   __snake_case       )

         snake_case											=							torch.tensor(
             [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]     ,   device=__snake_case     ,   )
         snake_case											=							torch.tensor(
             [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]     ,   device=__snake_case       )
         self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]     ,   __snake_case     ,   atol=1E-4       )       )
         self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]     ,   __snake_case     ,   atol=1E-4       )       )

         # verify postprocessing
         snake_case											=							image_processor.post_process_object_detection(
             __snake_case     ,   threshold=0.3     ,   target_sizes=[image.size[::-1]]       )[0]
         snake_case											=							torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]       ).to(__snake_case       )
         snake_case											=							[7_5, 7_5, 1_7, 6_3, 1_7]
         snake_case											=							torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]       ).to(__snake_case       )

         self.assertEqual(len(results['''scores''']       )     ,   5       )
         self.assertTrue(torch.allclose(results['''scores''']     ,   __snake_case     ,   atol=1E-4       )       )
         self.assertSequenceEqual(results['''labels'''].tolist()     ,   __snake_case       )
         self.assertTrue(torch.allclose(results['''boxes'''][0, :]     ,   __snake_case       )       )








import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							StableDiffusionPipeline.from_pretrained(UpperCamelCase_				,torch_dtype=torch.floataa							)

     # load LoRA weight from .safetensors
     snake_case											=							load_file(UpperCamelCase_							)

     snake_case											=							[]

     # directly update weight in diffusers model
     for key in state_dict:
          # it is suggested to print out the key, it usually will be something like below
          # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

          # as we have set the alpha beforehand, so just skip
          if ".alpha" in key or key in visited:
               continue

          if "text" in key:
               snake_case											=							key.split('''.'''							)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''							)[-1].split('''_'''							)
               snake_case											=							pipeline.text_encoder
          else:
               snake_case											=							key.split('''.'''							)[0].split(LORA_PREFIX_UNET + '''_'''							)[-1].split('''_'''							)
               snake_case											=							pipeline.unet

          # find the target layer
          snake_case											=							layer_infos.pop(0							)
          while len(UpperCamelCase_							) > -1:
               try:
                    snake_case											=							curr_layer.__getattr__(UpperCamelCase_							)
                    if len(UpperCamelCase_							) > 0:
                         snake_case											=							layer_infos.pop(0							)
                    elif len(UpperCamelCase_							) == 0:
                         break
               except Exception:
                    if len(UpperCamelCase_							) > 0:
                         temp_name += "_" + layer_infos.pop(0							)
                    else:
                         snake_case											=							layer_infos.pop(0							)

          snake_case											=							[]
          if "lora_down" in key:
               pair_keys.append(key.replace('''lora_down'''				,'''lora_up'''							)							)
               pair_keys.append(UpperCamelCase_							)
          else:
               pair_keys.append(UpperCamelCase_							)
               pair_keys.append(key.replace('''lora_up'''				,'''lora_down'''							)							)

          # update weight
          if len(state_dict[pair_keys[0]].shape							) == 4:
               snake_case											=							state_dict[pair_keys[0]].squeeze(3							).squeeze(2							).to(torch.floataa							)
               snake_case											=							state_dict[pair_keys[1]].squeeze(3							).squeeze(2							).to(torch.floataa							)
               curr_layer.weight.data += alpha * torch.mm(UpperCamelCase_				,UpperCamelCase_							).unsqueeze(2							).unsqueeze(3							)
          else:
               snake_case											=							state_dict[pair_keys[0]].to(torch.floataa							)
               snake_case											=							state_dict[pair_keys[1]].to(torch.floataa							)
               curr_layer.weight.data += alpha * torch.mm(UpperCamelCase_				,UpperCamelCase_							)

          # update visited list
          for item in pair_keys:
               visited.append(UpperCamelCase_							)

     return pipeline


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE     :					Dict      =       argparse.ArgumentParser()

 parser.add_argument(
     "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
 )
 parser.add_argument(
     "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
 )
 parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
 parser.add_argument(
     "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
 )
 parser.add_argument(
     "--lora_prefix_text_encoder",
     default="lora_te",
     type=str,
     help="The prefix of text encoder weight in safetensors",
 )
 parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
 parser.add_argument(
     "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
 )
 parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")

 _SCREAMING_SNAKE_CASE     :					List[Any]      =       parser.parse_args()

 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       args.base_model_path
 _SCREAMING_SNAKE_CASE     :					Dict      =       args.checkpoint_path
 _SCREAMING_SNAKE_CASE     :					List[str]      =       args.dump_path
 _SCREAMING_SNAKE_CASE     :					Dict      =       args.lora_prefix_unet
 _SCREAMING_SNAKE_CASE     :					List[str]      =       args.lora_prefix_text_encoder
 _SCREAMING_SNAKE_CASE     :					Optional[Any]      =       args.alpha

 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

 _SCREAMING_SNAKE_CASE     :					Tuple      =       pipe.to(args.device)
 pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)




